AI-Driven Digital Asset Co-pilot Apparatuses, Mechanisms, Mediums, Processes and Systems

Abstract

The AI-Driven Digital Asset Co-pilot Apparatuses, Mechanisms, Mediums, Processes and Systems (“AIDAC”) transforms temporal quantum limited asset value request, temporal quantum limited asset fill request, ML engine training request, AI task processing request datastructure/inputs via AIDAC components into temporal quantum limited asset value response, temporal quantum limited asset fill response, ML engine training response, AI task processing response datastructure/outputs. A task processing request datastructure is obtained. A set of subtasks for the task is determined via an orchestration artificial intelligence engine. A subtask execution generative AI engine for each subtask is determined. A relevant subtask dataset is determined for each subtask and incorporated into an execution context of the subtask execution generative AI engine to utilize for the respective subtask. A subtask execution result is obtained for each subtask and evaluated for acceptability. A task execution result is composited via the subtask execution results via the orchestration artificial intelligence engine.

What is claimed is: 
     
         1 . An AI task processing apparatus, comprising:
 at least one memory;   a component collection stored in the at least one memory;   any of at least one processor disposed in communication with the at least one memory, the any of at least one processor executing processor-executable instructions from the component collection, storage of the component collection structured with processor-executable instructions comprising:
 obtain a task processing request datastructure, in which the task processing request datastructure is structured as specifying task instructions for a task; 
 determine a set of subtasks for the task by analyzing the task instructions via an orchestration generative AI engine, in which a subtask corresponds to a function specified via a predefined schema incorporated into an execution context of the orchestration generative AI engine; 
 determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution generative AI engine to utilize for the respective subtask; 
 determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a relevant subtask dataset for the respective subtask, in which the relevant subtask dataset for the respective subtask is incorporated into an execution context of the subtask execution generative AI engine to utilize for the respective subtask; 
 obtain via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution result for the respective subtask from the subtask execution generative AI engine to utilize for the respective subtask; 
 evaluate via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, acceptability of the subtask execution result for the respective subtask; and 
 composite via the orchestration artificial intelligence engine, a task execution result for the task via one or more of the obtained subtask execution results. 
   
     
     
         2 . The apparatus of  claim 1 , in which the task instructions comprise a free text user prompt. 
     
     
         3 . The apparatus of  claim 1 , in which the task instructions comprise one of: a GUI command, a CLI command, an API command. 
     
     
         4 . The apparatus of  claim 1 , in which the instructions to determine the set of subtasks for the task further comprise instructions to:
 determine a task template associated with the task; and   in which the set of subtasks for the task is determined via the task template.   
     
     
         5 . The apparatus of  claim 1 , in which the instructions to determine the subtask execution generative AI engine to utilize for the respective subtask are structured as instructions to determine a best performing subtask execution generative AI engine for a function corresponding to the respective subtask. 
     
     
         6 . The apparatus of  claim 1 , in which the instructions to determine the subtask execution generative AI engine to utilize for the respective subtask are structured as instructions to determine a plurality of subtask execution generative AI engines to utilize in parallel for the respective subtask. 
     
     
         7 . The apparatus of  claim 1 , in which a relevant subtask dataset comprises relevant historical data, relevant on-demand data, and relevant entity data associated with an entity or a user specified via the task processing request datastructure. 
     
     
         8 . The apparatus of  claim 1 , in which the instructions to obtain the subtask execution result for the respective subtask further comprise instructions to provide subtask execution instructions generated via the orchestration generative AI engine to the subtask execution generative AI engine to utilize for the respective subtask. 
     
     
         9 . The apparatus of  claim 1 , in which the instructions to obtain the subtask execution result for the respective subtask further comprise instructions to provide subtask execution instructions, specified via a function of the predefined schema corresponding to the respective subtask, to the subtask execution generative AI engine to utilize for the respective subtask. 
     
     
         10 . The apparatus of  claim 1 , in which the acceptability of a subtask execution result is evaluated via one or more of AI reasoning, validation mechanisms, structured checks. 
     
     
         11 . The apparatus of  claim 1 , in which the instructions to evaluate acceptability of the subtask execution result for the respective subtask further comprise instructions to determine via the orchestration generative AI engine corrective measures for the respective subtask upon determining that the subtask execution result for the respective subtask is not acceptable. 
     
     
         12 . The apparatus of  claim 11 , in which the corrective measures for the respective subtask comprise corrective instructions to utilize for the subtask execution generative AI engine to utilize for the respective subtask. 
     
     
         13 . The apparatus of  claim 11 , in which the corrective measures for the respective subtask comprise a selection of another subtask execution generative AI engine to utilize for the respective subtask. 
     
     
         14 . The apparatus of  claim 11 , in which the corrective measures for the respective subtask comprise obtaining an additional relevant subtask dataset for the respective subtask. 
     
     
         15 . The apparatus of  claim 1 , in which the storage of the component collection is further structured with processor-executable instructions comprising:
 augment via the orchestration artificial intelligence engine, the task execution result for the task with a recommended action determined via analysis of the task execution result.   
     
     
         16 . An AI task processing processor-readable, non-transient medium, the medium storing a component collection, storage of the component collection structured with processor-executable instructions comprising:
 obtain a task processing request datastructure, in which the task processing request datastructure is structured as specifying task instructions for a task;   determine a set of subtasks for the task by analyzing the task instructions via an orchestration generative AI engine, in which a subtask corresponds to a function specified via a predefined schema incorporated into an execution context of the orchestration generative AI engine;   determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution generative AI engine to utilize for the respective subtask;   determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a relevant subtask dataset for the respective subtask, in which the relevant subtask dataset for the respective subtask is incorporated into an execution context of the subtask execution generative AI engine to utilize for the respective subtask;   obtain via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution result for the respective subtask from the subtask execution generative AI engine to utilize for the respective subtask;   evaluate via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, acceptability of the subtask execution result for the respective subtask; and   composite via the orchestration artificial intelligence engine, a task execution result for the task via one or more of the obtained subtask execution results.   
     
     
         17 . An AI task processing processor-implemented system, comprising:
 means to store a component collection;   means to process processor-executable instructions from the component collection, storage of the component collection structured with processor-executable instructions comprising:
 obtain a task processing request datastructure, in which the task processing request datastructure is structured as specifying task instructions for a task; 
 determine a set of subtasks for the task by analyzing the task instructions via an orchestration generative AI engine, in which a subtask corresponds to a function specified via a predefined schema incorporated into an execution context of the orchestration generative AI engine; 
 determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution generative AI engine to utilize for the respective subtask; 
 determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a relevant subtask dataset for the respective subtask, in which the relevant subtask dataset for the respective subtask is incorporated into an execution context of the subtask execution generative AI engine to utilize for the respective subtask; 
 obtain via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution result for the respective subtask from the subtask execution generative AI engine to utilize for the respective subtask; 
 evaluate via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, acceptability of the subtask execution result for the respective subtask; and 
 composite via the orchestration artificial intelligence engine, a task execution result for the task via one or more of the obtained subtask execution results. 
   
     
     
         18 . An AI task processing process, including processing processor-executable instructions via any of at least one processor from a component collection stored in at least one memory, storage of the component collection structured with processor-executable instructions comprising:
 obtain a task processing request datastructure, in which the task processing request datastructure is structured as specifying task instructions for a task;   determine a set of subtasks for the task by analyzing the task instructions via an orchestration generative AI engine, in which a subtask corresponds to a function specified via a predefined schema incorporated into an execution context of the orchestration generative AI engine;   determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution generative AI engine to utilize for the respective subtask;   determine via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a relevant subtask dataset for the respective subtask, in which the relevant subtask dataset for the respective subtask is incorporated into an execution context of the subtask execution generative AI engine to utilize for the respective subtask;   obtain via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, a subtask execution result for the respective subtask from the subtask execution generative AI engine to utilize for the respective subtask;   evaluate via the orchestration artificial intelligence engine, for each respective subtask in the set of subtasks, acceptability of the subtask execution result for the respective subtask; and   composite via the orchestration artificial intelligence engine, a task execution result for the task via one or more of the obtained subtask execution results.